H∞ circle criterion observer design for Lipschitz nonlinear systems with enhanced LMI conditions

2016 ◽  
Author(s):  
A. Zemouche ◽  
R. Rajamani ◽  
B. Boulkroune ◽  
H. Rafaralahy ◽  
M. Zasadzinski
Keyword(s):  
Nonlinear Systems ◽  
Observer Design ◽  
Circle Criterion ◽  
Lipschitz Nonlinear Systems

Robust observer design of one-sided Lipschitz nonlinear systems

2018 ◽  
Author(s):  
Badreddine El Haiek ◽  
Hicham El Aiss ◽  
Abdelaziz Hmamed ◽  
Ahmed El Hajjaji
Keyword(s):  
Nonlinear Systems ◽  
Observer Design ◽  
Robust Observer ◽  
Lipschitz Nonlinear Systems

LMI Enhanced Observer Design for Lipschitz Nonlinear Systems

2013 ◽  
Vol 756-759 ◽  
pp. 420-424
Author(s):  
Feng Qiao ◽  
Qing Ma ◽  
Feng Zhang ◽  
Hao Ming Zhao
Keyword(s):  
Nonlinear Systems ◽  
Observer Design ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Simulation Studies ◽  
Complex Issue ◽  
Lipschitz Nonlinear Systems ◽  
Satisfy Lipschitz Condition ◽  
The Relationship ◽  
Selection Of

Observer design for nonlinear systems has been an important and complex issue for decades. In this paper, considering a class of nonlinear systems which satisfy Lipschitz condition, a method for observer design is investigated based on Linear Matrix Inequality (LMI). This study focuses on the selection of gain matrices using LMI for two kinds of Lipschitz nonlinear systems, which are classified by the relationship between output and state. Simulation studies are made with Matlab/Simulink in this paper, and the simulation results verify the effectiveness of the proposed method.

Robust Observer Design for Lipschitz Nonlinear Systems With Parametric Uncertainty

2013 ◽  
Author(s):  
Yan Wang ◽  
David M. Bevly
Keyword(s):  
Nonlinear Systems ◽  
Vector Fields ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Performance Criteria ◽  
Adaptation Algorithm ◽  
Algebraic Set ◽  
Observer Error ◽  
Robust Observer ◽  
Lipschitz Nonlinear Systems

This paper discusses optimal and robust observer design for the Lipschitz nonlinear systems. The stability analysis for the Lure problem is first reviewed. Then, a two-DOF nonlinear observer is proposed so that the observer error dynamic model can be transformed to an equivalent Lure system. In this framework, the difference of the nonlinear parts in the vector fields of the original system and observer is modeled as a nonlinear memoryless block that is covered by a multivariable sector condition or an equivalent semi-algebraic set defined by a quadratic polynomial inequality. Then, a sufficient condition for asymptotic stability of the observer error dynamics is formulated in terms of the feasibility of polynomial matrix inequalities (PMIs), which can be solved by Lasserre’s moment relaxation. Furthermore, various quadratic performance criteria, such as H2 and H∞, can be easily incorporated in this framework. Finally, a parameter adaptation algorithm is introduced to cope with the parameter uncertainty.

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